1. Field of Invention
The invention relates to an image data processing technique and, in particular, to an image processing technique that uses a neural network to recognize text pixels and picture pixels in an image, thereby achieving the goal of text/picture separation.
2. Related Art
The multi-function peripheral (MFP) is an image processing device that combines such functions as scanning, copying, and printing. Since it integrates various kinds of useful functions in one machine, it gradually becomes one of the most favorite products on the market. In particular, the copy function of the MFP actually combines the functions of scanning and printing. For example, in the copy mode, the scanning function is initiated. Its charge coupled device (CCD) scans a document to obtain image data. Afterwards, the print function is initiated to print the scanned image data in ink (carbon powders).
Since the RGB color correction of the CCD is not sensitive to the edges of black text, it has to mix ink of the CMY colors when printing the black text. This does not only waste color ink, but also slows down the printing speed because it has to mix ink of the three colors to print the black text. Moreover, the quality is not satisfactory; black text often becomes color text.
The image data of a document usually contains the text and picture parts. In order to have a better visual or output effect, the image data has to be blurred. However, blurring the whole image will deteriorate the text quality. Therefore, it would be desirable to separate the text and picture parts in the image. One can then blur the picture part, and may further perform edge enhancement in the text part. When printing the document, one can use only the black ink (or carbon powders) for the text part. This can save the ink (carbon) cost and increase the printing speed. The printing quality of the black text also becomes better.
There are many techniques for separating text and pictures in an image. However, most techniques have to convert the image data to use the coordinate system in the frequency domain. This requires the use of complicated hardware, and thus a higher cost. The method of using a neural network technique to separate text and pictures in image data can process data in the coordinate system of the spatial domain, without converting to the frequency domain.
Nonetheless, the neural network technique for separating text and pictures in an image proposed in the prior art has to perform a preparation step of characteristic quantization. Each variable (input value of the neural network) in the image data has to be pre-processed in order for the neural network to process image data, then determining which type (text, picture or noise) the image data belongs to.
According to the prior art, the technique of separating text and pictures in an image has to pre-process the image data and has to use two (or more than two) different devices to process image data. Under the consideration of speed and memory, the prior is obvious not suitable for the MFP.